Job Scheduling Model Research Articles

Deep-Reinforcement-Learning-Based Production Scheduling in Industrial Internet of Things

The unprecedented prosperity of the Industrial Internet of Things (IIoT) promotes the traditional industry transforming into intelligent manufacturing so that the whole production process can be comprehensively controlled to achieve flexible production. Intelligent scheduling, as one of the key enabling techniques, is desired to allocate the production of several machines by an efficient solution with minimum makespan. Existing approaches adopt a fixed search paradigm based on expert knowledge to seek satisfactory solutions. However, considering the varying data distribution and large-sized of the practical problems, these methods fail to guarantee the quality of the obtained solution under the real-time requirement. To address this challenge, we formulate the production scheduling problem as a Markov decision process (MDP) and specifically design a job scheduling model made up of a job batching module for the hybrid flow-shop scheduling problem on batch processing machines (HFSP-BPM). Our proposed model consists of an actor network that learns the action under different conditions and a critic network that evaluates the action of the actor. We analyze the convergence of the model under different parameter settings to determine the optimal parameter. Extensive numerical experiments on both publicly available dataset and real steel plant production dataset demonstrate that the proposed deep reinforcement learning (DRL) approach compared with other baselines, more than 6% average improvements can be observed in many instances.

Job-scheduling Model For an Autonomous Additive Manufacturing: a Case of 3D Food Printing

Three-dimensional food printing (3DFP) is an emerging application of additive manufacturing (AM) as it offers customized designs, personalized nutrition, simplified and efficient supply chain, and reduced waste. Grouping parts into jobs (batches) and sequencing these jobs (known as job-scheduling) is considered a key operational factor in additive manufacturing (AM). The task of job-scheduling is complex because the number of possible solutions grows exponentially with the number of parts and various constraints has to be considered (e.g., machine's capacity). In the case of 3DFP, scheduling presents a unique challenge since different 3D printed foods have different time constraints depending on material's shelf-life. In this paper, we propose a mathematical optimization model for job-scheduling in 3DFP that minimizes the makespan (total accumulative manufacturing time) and the deadline violation simultaneously. We reformulate the proposed optimization model to the form of mixed-integer linear programming (MILP) and solve in using MILP solver in MATLAB 2022. We demonstrate the effectiveness of the proposed model with a numerical case study. The obtained results show that our algorithm obtains scheduling that significantly reduces the deadline violation while achieving the same production time as the state-of-the-art baseline.

Malleable scheduling beyond identical machines

In malleable job scheduling, jobs can be executed simultaneously on multiple machines with the processing time depending on the number of allocated machines. In this setting, jobs are required to be executed non-preemptively and in unison, in the sense that they occupy, during their execution, the same time interval over all the machines of the allocated set. In this work, we study generalizations of malleable job scheduling inspired by standard scheduling on unrelated machines. Specifically, we introduce a general model of malleable job scheduling, where each machine has a (possibly different) speed for each job, and the processing time of a job j on a set of allocated machines S depends on the total speed of S with respect to j. For machines with unrelated speeds, we show that the optimal makespan cannot be approximated within a factor less than \(\frac{e}{e-1}\), unless \(P = NP\). On the positive side, we present polynomial-time algorithms with approximation ratios \(\frac{2e}{e-1}\) for machines with unrelated speeds, 3 for machines with uniform speeds, and 7/3 for restricted assignments on identical machines. Our algorithms are based on deterministic LP rounding. They result in sparse schedules, in the sense that each machine shares at most one job with other machines. We also prove lower bounds on the integrality gap of \(1+\varphi \) for unrelated speeds (\(\varphi \) is the golden ratio) and 2 for uniform speeds and restricted assignments. To indicate the generality of our approach, we show that it also yields constant factor approximation algorithms for a variant where we determine the effective speed of a set of allocated machines based on the \(L_p\) norm of their speeds.

Dynamic Job Scheduling Strategy Using Jobs Characteristics in Cloud Computing

A crucial performance concern in distributed decentralized environments, like clouds, is how to guarantee that jobs complete their execution within the estimated completion times using the available resources’ bandwidth fairly and efficiently while considering the resource performance variations. Formerly, several models including reservation, migration, and replication heuristics have been implemented to solve this concern under a variety of scheduling techniques; however, they have some undetermined obstacles. This paper proposes a dynamic job scheduling model (DTSCA) that uses job characteristics to map them to resources with minimum execution time taking into account utilizing the available resources bandwidth fairly to satisfy the cloud users quality of service (QoS) requirements and utilize the providers’ resources efficiently. The scheduling algorithm makes use of job characteristics (length, expected execution time, expected bandwidth) with regards to available symmetrical and non-symmetrical resources characteristics (CPU, memory, and available bandwidth). This scheduling strategy is based on generating an expectation value for each job that is proportional to how these job’s characteristics are related to all other jobs in total. That should make their virtual machine choice closer to their expectation, thus fairer. It also builds a feedback method which deals with reallocation of failed jobs that do not meet the mapping criteria.

A static VM placement and hybrid job scheduling model for green data centers.

Reducing energy consumption has become a critical issue in today data centers. Reducing the number of required physical and Virtual Machines results in energy-efficiency. In this paper, to avoid the disadvantages of VM migration, a static VM placement algorithm is proposed which places VMs on hosts in a Worst-Fit-Decreasing (WFD) fashion. To reduce energy consumption further, the effect of job scheduling policy on the number of VMs needed for maintaining QoS requirements is studied. Each VM is modeled by an M/M/* queue in space-shared, time-shared, and hybrid job scheduling policies, and energy consumption of real-time as well as non-real-time applications is analyzed. Numerical results show that the hybrid policy outperforms space-shared and time-shared policies, in terms of energy consumption as well as Service Level Agreement (SLA) violations. Moreover, our non-migration method outperforms three different algorithms which use VM migration, in terms of reducing both energy consumption and SLA Violations.

Real-coded multi-objective genetic algorithm with effective queuing model for efficient job scheduling in heterogeneous Hadoop environment

In the recent past, cloud infrastructure has considerably increased its applicability. This has resulted in effective Big Data processing. Hadoop schedulers are critical components to deliver required levels of efficiency, where MapReduce tasks are assigned to Hadoop nodes by the scheduler. There is a significant challenge in planning the increasing amount of functions and resources in a scalable way. Also, this challenge is further compounded by the potential heterogeneity of the deployed Hadoop. This paper proposes a scheduler that makes scheduling a choice by assessing the entire task group in the job queue. Further, the proposed scheduler uses a new scheduling method based on Real Coded Genetic Algorithm (RCGA). RCGA with MapReduce enables users to create more scalable applications. A higher abstraction is provided in less time. The experimental results indicate that the proposed RCGA scheduler achieves better performance than the existing systems for the following metrics: Execution Time, Total Cost, Resource Utilization, Speedup, Throughput, Scheduling Efficiency, Fairness Relaxation, Scheduling Time, Turnaround Time, CPU Time, Data Locality and Average Node-Locality Ratio.

An Enhanced Model for Job Sequencing and Dispatch in Identical Parallel Machines

This paper has developed an efficient scheduling model that is robust and minimizes the total completion time for job completion in identical parallel machines. The new model employs Genetic-Fuzzy technique for job sequencing and dispatch in identical parallel machines. It uses genetic algorithm technique to develop a job scheduler that does the job sequencing and optimization while fuzzy logic technique was used to develop a job dispatcher that dispatches job to the identical parallel machines. The methodology used for the design is the Object Oriented Analysis and Design Methodology (OOADM) and the system was implemented using C# and .NET framework. The model was tested with fifteen identical parallel machines used for printing. The parameters used in analyzing this model include the job scheduling length, average execution time, load balancing and machines utilization. The result generated from the developed model was compare with the result of other job scheduling models like First Come First Sever (FCFS) scheduling approach and Genetic Model (GA) scheduling approach. The result of the new model shows a better load balancing and high machine utilization among the individual machines when compared with the First Come First Serve (FCFS) scheduling model and Genetic Algorithm (GA) scheduling model. Keywords : Parallel Machines, Genetic Model, Job Scheduler, Fuzzy Logic Technique, Load Balancing, Machines Utilization DOI: 10.7176/CEIS/11-2-05 Publication date: March 31 st 2020

A Job Sequencing Problem of an Overhead Shuttle Crane in a Rail-Based Automated Container Terminal

This study proposes a job scheduling model and its heuristics for an automated container terminal with an overhead shuttle crane (OS) to reduce the total tardiness time of flatcars and external trucks by considering the separation of each job into a main job, and a premarshaling or remarshaling job. The OS is busy or idle according to the fluctuations in the processing times of different pieces of equipment. We identify the OS job sequencing problem considering job separation (OSJSPS) as a mixed-integer programming (MIP) model, which simultaneously sequences a set of jobs and searches for their possible separation into premarshaling and remarshaling jobs. We present a two-stage genetic algorithm (TGA) based on two local improvement procedures: an iterative local search procedure and an opportunistic job separation procedure. We conclude that the two-stage genetic algorithm reduces the total tardiness time of the container terminal’s flatcars and external trucks as the number of OS jobs increases.

Parallel Simulation of Tasks Scheduling and Scheduling Criteria in High-performance Computing Systems

This work is focused on the issue of job scheduling in a high performance computing systems. The goal is based on the analysis of scheduling models of tasks in grid and cloud, design and implementation of the simulator on the base of GPGPU. The simulator is verified by our own proposed model of job scheduling. The simulator consists of a centralized scheduler that is using GPGPU to process large amounts of data by parallel way. In order to ensure the optimization of the scheduling process we have implemented a simulated annealing algorithm. GPGPU model was compared to the CPU when the number of nodes from 32 to 2048. Improving the implementation based on GPGPU had a significant impact on the system with 512 nodes and with an increasing number of nodes further accelerates in comparison with sequential algorithm. In this work are designed new scheduling criteria which are experimentally evaluated.

DAG-CPM Scheduler for Parallel Execution of Critical Jobs

Map Reduce applications having multiple jobs may be dependent on each other such as iterative Page View application [2] performs the required operation in several iterations before generating the result. Each iteration is considered as single job. Conventional Hadoop MapReduce schedules the jobs sequentially, but not customized to handle multi job application. Also, it will not perform the parallel execution of the dependent jobs. This prolongs the execution time to complete all the jobs. Therefore a new scheduler DAG–CPM Scheduler uses the critical path job scheduling model, to identify the jobs present in the critical path. Critical path job scheduling is optimized to offer support for multi job applications, critical path job is a series of jobs, if execution of a job is delayed, then time required to execute all jobs will be prolonged. DAG–CPM Scheduler schedules multiple jobs by dynamically constructing the job dependency in DAG for the currently running job based on the input and output of a job. DAG represents the dependency among the jobs, this dependency graph is used to insert a pipeline between the output of one job as input for map tasks of another job and it executes the dependent jobs in parallel which results into a substantial reduction in the execution time of an application. Experimental analysis on the proposed approach has been carried out on Page View application on Academic and research web server log file, such as, NASA and rnsit.ac.in of 10 GB data set. PigMix2 is executed on 8GB data set. Experimental results reveal that the average execution time is decreased by 41% compared to Hadoop in respect Page View application and Execution speed is 37.7% faster compared to Pig and DAG–CPM Scheduler can run 24.3% faster when compared to DAG–CPM Scheduler without pipeline.

Job Scheduling for Hybrid Assembly Differentiation Flow Shop to Minimize Total Actual Flow Time considering Multi-Due-Dates

The hybrid assembly differentiation flow shop is a three-stage flow shop system with machining, assembly and differentiation stages. The machining stage produces k parts in independently unrelated dedicated machines. After k parts of a job completed, they are assembled at the assembly stage and assigned to dedicated machines for a final process. At the end, several types of finished products are resulted. The finished products should be delivered at their respective different due dates. The problem is to find a schedule considering multi due dates, and the criterion of minimizing total actual flow time. This paper proposes a model for job scheduling in a hybrid assembly differentiation flow shops and its algorithm for solving the problems. The initial solution is defined using SPT-based heuristic and it is optimized using variable neighbourhood descend method. The proposed model is tested using a set of hypothetic data. The solution shows that the algorithm can solve the problems effectively.

SCARL: Attentive Reinforcement Learning-Based Scheduling in a Multi-Resource Heterogeneous Cluster

Advanced reinforcement learning (RL) technologies have recently increased the opportunity for automating several tasks in cluster management at scale by exploiting repetitive logs of cluster operation and building a learning model for resource allocation and job scheduling. Yet, this trend of adopting RL in the domain of cluster management has not fully addressed the diversity and heterogeneity of jobs and machines in modern cluster environments. In this paper, we present an RL-based scheduler for a multi-resource cluster, namely SCARL (SCheduler with Attentive Reinforcement Learning), concentrating on intricate cluster operating conditions with different resource requirements and capabilities. Specifically, we employ attentive embedding and factored-action scheduling that together efficiently incorporate time-varying interdependency of jobs and machines in RL processing; they enable an end-to-end scalable policy for scheduling diverse jobs on heterogeneous machines. To the best of our knowledge, we are the first to employ attention mechanism in RL-based cluster resource management. Through experiments, we demonstrate that our approach is competitive with existing heuristic methods under various cluster simulation configurations, e.g., an average 9.2 % enhancement in slowdown over the shortest job first algorithm. Additionally, the approach yields stable performance with our test cluster for running synthetic workloads based on real traces.

A dynamic cluster job scheduling optimisation algorithm based on data irreversibility in sensor cloud

The optimisation algorithm based on irreversible data for the job scheduling of dynamic cluster is crucial to the improvement of the cluster rendering throughput and the cluster rendering efficiency. However, the dispatching imbalance of the cluster rendering task on the massive number of cluster rendering nodes will prolong the waiting time for the completion of job. Therefore, we propose dynamic cluster job scheduling optimisation algorithm based on data irreversibility (DCJS_DI). Firstly, we analyse the job scheduling target. Then, we exploit the frame independence with the clustering rendering and further establish the job scheduling model for the irreversible data dynamic cluster. Next, we elaborate on the job scheduling process of the irreversible data dynamic cluster and the dispatching process of the cluster rendering task. Finally, we investigate via simulation results the impacts of the job hunger and the resource fragmentation issue of the traditional job scheduling strategies on the system performance, the impacts of the multi-progress and multi-threading cluster rendering on the job completion time and the resource efficiency of the irreversible data dynamic cluster. We further study the extension of the cluster computation capability and the reliability issue of the cloud service.

PISCES: Optimizing Multi-Job Application Execution in MapReduce

Nowadays, many MapReduce applications consist of groups of jobs with dependencies among each other, such as iterative machine learning applications and large database queries. Unfortunately, the MapReduce framework is not optimized for these multi-job applications. It does not explore the execution overlapping opportunities among jobs and can only schedule jobs independently. These issues significantly inflate the application execution time. This paper presents Pipeline Improvement Support with Critical chain Estimation Scheduling (PISCES), a critical chain optimization (a critical chain refers to a series of jobs which will make the application run longer if any one of them is delayed), to provide better support for multi-job applications. PISCES extends the existing MapReduce framework to allow scheduling for multiple jobs with dependencies by dynamically building up a job dependency DAG for current running jobs according to their input and output directories. Then using the dependency DAG, it provides an innovative mechanism to facilitate the data pipelining between the output phase (map phase in the Map-Only job or reduce phase in the Map-Reduce job) of an upstream job and the map phase of a downstream job. This offers a new execution overlapping between dependent jobs in MapReduce which effectively reduces the application runtime. Moreover, PISCES proposes a novel critical chain job scheduling model based on the accurate critical chain estimation. Experiments show that PISCES can increase the degree of system parallelism by up to 68 percent and improve the execution speed of applications by up to 52 percent.

A dynamic cluster job scheduling optimisation algorithm based on data irreversibility in sensor cloud

The optimisation algorithm based on irreversible data for the job scheduling of dynamic cluster is crucial to the improvement of the cluster rendering throughput and the cluster rendering efficiency. However, the dispatching imbalance of the cluster rendering task on the massive number of cluster rendering nodes will prolong the waiting time for the completion of job. Therefore, we propose dynamic cluster job scheduling optimisation algorithm based on data irreversibility (DCJS_DI). Firstly, we analyse the job scheduling target. Then, we exploit the frame independence with the clustering rendering and further establish the job scheduling model for the irreversible data dynamic cluster. Next, we elaborate on the job scheduling process of the irreversible data dynamic cluster and the dispatching process of the cluster rendering task. Finally, we investigate via simulation results the impacts of the job hunger and the resource fragmentation issue of the traditional job scheduling strategies on the system performance, the impacts of the multi-progress and multi-threading cluster rendering on the job completion time and the resource efficiency of the irreversible data dynamic cluster. We further study the extension of the cluster computation capability and the reliability issue of the cloud service.

Using clustering approaches for response time aware job scheduling model for internet of things (IoT)

Internet of things (IoT) is the next evolution of internet connecting and transferring information between smart things/objects covering daily aspects of life. This is realized with the involvement of large number of sensor nodes which monitors, generates and enables aggregation of data. In such an environment, clustering becomes essential by grouping a structure of objects with similar attributes. Clustering, helps in establishing topologies which in turn can be used for optimizing the quality of service (QoS) parameters while managing the resources in the underlying dynamic and heterogeneous IoT network environment. This work proposes to study and compare K-means, hierarchical clustering and fuzzy C-means clustering (FCM) algorithms to design a response time aware scheduling model for IoT. The work intends to improve the QoS by routing the data through clusters formed using the above three algorithms to observe the effect of clustering on the response time aiming to minimize the same. Establishing the clustering scheme with optimum response time results in optimizing the scheduling performance of the underlying network too by minimizing the overall execution cost. The effect on message scheduling to account for the prioritized message delivery has been studied. Simulation study proves the efficiency of the K-means clustering approach under various test conditions.

A Load Balancing Model for Job Scheduling using Graph Theory and Allocation of Time

A Load Balancing Model for Job Scheduling using Graph Theory and Allocation of Time

Strategy optimization of resource scheduling based on cluster rendering

Job scheduling strategy of rendering was studied according to the scheduling model of heterogeneous cluster rendering system. Then, the work proposed the job scheduling strategy for heterogeneous cluster rendering as follows: Definition of load balancing measurement, influencing factors of model, construction of job scheduling model based on cluster rendering, and dynamic algorithm design based on genetic algorithm. The heterogeneous cluster rendering system achieved the effective scheduling algorithm for load balancing. The simulation results showed the algorithm had good load balancing. The work proposes the theoretical basis and new idea for resource scheduling of cluster system and cloud platform as well as the support for the effective use of resources. It has great significance.

A Multi-objective Mathematical Model for Job Scheduling on Parallel Machines Using NSGA-II

A Multi-objective Mathematical Model for Job Scheduling on Parallel Machines Using NSGA-II

A genetic algorithm-based job scheduling model for big data analytics.

Big data analytics (BDA) applications are a new category of software applications that process large amounts of data using scalable parallel processing infrastructure to obtain hidden value. Hadoop is the most mature open-source big data analytics framework, which implements the MapReduce programming model to process big data with MapReduce jobs. Big data analytics jobs are often continuous and not mutually separated. The existing work mainly focuses on executing jobs in sequence, which are often inefficient and consume high energy. In this paper, we propose a genetic algorithm-based job scheduling model for big data analytics applications to improve the efficiency of big data analytics. To implement the job scheduling model, we leverage an estimation module to predict the performance of clusters when executing analytics jobs. We have evaluated the proposed job scheduling model in terms of feasibility and accuracy.